Does the Effect of Pollution on Infant Mortality Differ Between Developing and Developed Countries? Evidence from Mexico City

Arceo-Gomez, Hanna and Oliva

APEC 8990

Paper Presentations

September 12, 2024

Motivation

  • Air pollution is a significant issue many parts of the world
  • Studies have estimated the health effects of pollution in developed countries
  • Reason to believe this evidence may not be externally valid for developing countries

Research Question

What is the effect of pollution on infant mortality in the context of a developing country?

Overview

  • Context: Mexico 1997 – 2006
    • Rapidly industrializing during this time period
    • Also implemented policies aimed at improving air quality
  • Methods: fixed effects and IV using thermal inversions
  • Findings:
    • Evidence that pollution increases infant mortality
    • Compared to the US, larger marginal effects for CO and similar marginal effects for PM10
  • Concern: other factors may be correlated with health
    • weather, socio-economic status and changes in economic conditions

Data

Mexico City 1997 – 2006

  1. Neonatal and infant mortality
  2. Pollution
  3. Thermal inversions
  4. Temperature and weather

Empirical Strategy

Fixed Effects

Objective: estimate \beta_1 the relationship between pollution (P_{mw}) in a municipality (m) in a given week (w) and mortality per 100,000 live births (Y_{mw}) Y_{mw} = \beta_0 + \beta_1 P_{mw} + \alpha_m + \sigma_{mj} + \epsilon_{mw}

  • \alpha_m is a set of municipality fixed effects that control for permanent differences across municipalities
  • \sigma_{mj} is a set of bimonthly x municipality fixed effects, which control for common factors in a given two month block that could affect both pollution levels and infant mortality within a municipality

Empirical Strategy

Fixed Effects

Y_{mw} = \beta_0 + \beta_1 P_{mw} + \alpha_m + \sigma_{mj} + \epsilon_{mw}

Concerns:

  • Unobserved, time-varying differences across municipalities will bias \beta_1
  • Classical measurement error in the pollution variable will bias \beta_1 downwards

Empirical Strategy

Instrumental Variables

Objective: test whether inversions increase the concentrations of different types of pollutants \rightarrow use the number of thermal inversions in a given week (TI_w) to instrument for pollution

\begin{align*} P_{mw} &= \pi_0 + \pi_1 TI_w + \sum \pi_{2m} w + h(W_{mw}) + \alpha_m + \sigma_{mj} + \mu_{mw} \\ Y_{mw} &= \beta_0 + \beta_1 P_{mw} + \sum \beta_{2m} w + h(W_{mw}) + \alpha_m + \sigma_{mj} + \epsilon_{mw} \end{align*}

  • TI_w varies at the week level so week by year fixed effects are not identified
    • control for municipality-specific week by year trends (w)
  • municipality fixed effects (\alpha_m) control for time-invariant characteristics across municipalities
  • bimonthly x municipality fixed effects (\sigma_{mj}) control for seasonal effects within each municipality
  • h(W_{mw}) is a set of controls for temperature and weather conditions

Empirical Strategy

Instrumental Variables

\begin{align*} P_{mw} &= \pi_0 + \pi_1 TI_w + \sum \pi_{2m} w + h(W_{mw}) + \alpha_m + \sigma_{mj} + \mu_{mw} \\ Y_{mw} &= \beta_0 + \beta_1 P_{mw} + \sum \beta_{2m} w + h(W_{mw}) + \alpha_m + \sigma_{mj} + \epsilon_{mw} \end{align*}

Concerns: - Exclusion restriction - residual seasonal variation - Another concern is that models that estimate the effect of pollution on infant death within a short time frame, such as week, overstate the effect. -

Results

Fixed Effects

Results

IV First Stage

Results

IV

Results

Comparison with US

Summary

  • Statistically significant effects of pollution on infant mortality